This repository curates a comprehensive list of essential papers, articles, and blogs for individuals pursuing careers in Data Science, Machine Learning, and Deep Learning. It serves as a structured reading guide, categorizing foundational and advanced topics to facilitate learning and understanding for aspiring and practicing engineers and researchers.

How It Works

The collection is organized by sub-discipline within ML/DL, such as General ML, Recommender Systems, CNNs, NLP, and GANs. Each category lists key papers, often with a suggested reading order (indicated by medals), and links to relevant blog posts, surveys, and code implementations. This approach provides a guided learning path through critical research and practical applications.

Quick Start & Requirements

No installation or execution is required. This is a curated list of external resources.

Highlighted Details

• Extensive categorization covering Data Science, general ML, boosting, dimensionality reduction, optimization, recommender systems, neural networks, CNNs, Capsule Networks, image captioning, object detection, pose detection, NLP, GANs, GNNs, and Medical AI.
• Includes links to seminal papers, influential blogs (e.g., Distill.pub, Colah's Blog, Andrej Karpathy), and practical code implementations where available.
• Features a "Rabbit hole" section for deeper dives into specific topics like audio processing and GANs.
• Offers advice on tackling complex mathematical papers and encourages community contributions for updates and corrections.

Maintenance & Community

The repository was initiated in October 2019 and has seen periodic updates, with the last recorded update in February 2020. The author actively solicits contributions for broken links or missing papers.

Licensing & Compatibility

The repository itself does not contain code and is a collection of links. The licensing of the linked papers and resources is not specified.

Limitations & Caveats

The project's last update was in February 2020, indicating it may not reflect the most recent advancements in the rapidly evolving fields of ML and DL. The curated list is subjective and may not be exhaustive.